Infection with Mycobacterium tuberculosis (MTB) is a massive worldwide problem. The advent of Multiply Drug- Resistant and eXtensively Drug-Resistant strains (MDR and XDR MTB) has exacerbated the problem and has resulted in increased mortality and substantial morbidity. Other than bedaquiline, which was approved several years ago but with a black box warning, the last new MTB agent was rifampin. However, lately a number of new agents, some with unique mechanisms of action have entered the developmental pipeline. This will ultimately help with the therapy of MDR/XDR MTB. A large part of the difficulty in treating MTB is the duration of therapy. Fully susceptible strains requir 6 months of therapy while MDR/XDR strains require 18-24 months of therapy or longer. Such long therapeutic durations exacerbate problems with adherence, which is a major driver of resistance. Further, particularly with MDR/XDR MTB, therapy has many second line agents which are more toxic than first line drugs. It would pay massive public health dividends to be able to shorten therapy. While we have new agents entering the therapeutic armamentarium, little thought has been given to how to use them to improve cell kill, suppress resistance and, hence, have the possibility of shortening therapy. It is the overall goal of this Program to identify optimal regimens that fulfill the requirements of shortening therapy: most rapid cell kill, resistance suppression and activity against different metabolic states in which MTB exists (log-phase growth, acid-phase growth and Non-Replicative Persistent Phenotype-phase). There are three Projects and three Cores. The Projects involve evaluating combinations of MTB drugs in the Hollow Fiber Infection Model (HFIM), in murine models of infection and in the Cynomolgus macaque Non- Human Primate model (NHP). The Cores are the Administrative Core, Drug Assay Core and Mathematical Modeling Core. All Projects and Cores will interact and cross support. The HFIM has the flexibility to study all the metabolic states and to do so with human, murine and NHP drug profiles. A publication from our lab noted that animal drug profiles alter the activity of drugs on the pathogens being modeled. There has been speculation regarding the utility of animal system for reliability to design human trials. We will use the HFIM to generate data on combination therapy kill rates and resistance suppression in each metabolic state, using human and animal profiles. The mathematical modeling will allow direct identification of the impact of the different profiles on endpoints. These HFIM estimates can then be compared to the modeled data in the animal systems. Driving effect parameters with different profiles will allow further insight into what information can be reliably extracted to allow the best bridging to human infection. Sequencing of regimens, with the follow-on regimen being targeted at the organism states remaining after the first regimen and with resistance mechanisms of the two regimens being independent may be the best way to shorten therapy. This can be a general paradigm for future combination regimen development.